Self-Driving Car Engineer Nanodegree¶
Deep Learning¶
Project: Build a Traffic Sign Recognition Classifier¶
In this notebook, a template is provided for you to implement your functionality in stages, which is required to successfully complete this project. If additional code is required that cannot be included in the notebook, be sure that the Python code is successfully imported and included in your submission if necessary.
Note: Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to \n", "File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.
In addition to implementing code, there is a writeup to complete. The writeup should be completed in a separate file, which can be either a markdown file or a pdf document. There is a write up template that can be used to guide the writing process. Completing the code template and writeup template will cover all of the rubric points for this project.
The rubric contains "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. The stand out suggestions are optional. If you decide to pursue the "stand out suggestions", you can include the code in this Ipython notebook and also discuss the results in the writeup file.
Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode.
Step 0: Load The Data¶
# Load pickled data
import pickle
# TODO: Fill this in based on where you saved the training and testing data
training_file = 'train.p'
validation_file='valid.p'
testing_file = 'test.p'
with open(training_file, mode='rb') as f:
train = pickle.load(f)
with open(validation_file, mode='rb') as f:
valid = pickle.load(f)
with open(testing_file, mode='rb') as f:
test = pickle.load(f)
X_train, y_train = train['features'], train['labels']
X_valid, y_valid = valid['features'], valid['labels']
X_test, y_test = test['features'], test['labels']
Step 1: Dataset Summary & Exploration¶
The pickled data is a dictionary with 4 key/value pairs:
'features'is a 4D array containing raw pixel data of the traffic sign images, (num examples, width, height, channels).'labels'is a 1D array containing the label/class id of the traffic sign. The filesignnames.csvcontains id -> name mappings for each id.'sizes'is a list containing tuples, (width, height) representing the the original width and height the image.'coords'is a list containing tuples, (x1, y1, x2, y2) representing coordinates of a bounding box around the sign in the image. THESE COORDINATES ASSUME THE ORIGINAL IMAGE. THE PICKLED DATA CONTAINS RESIZED VERSIONS (32 by 32) OF THESE IMAGES
Complete the basic data summary below. Use python, numpy and/or pandas methods to calculate the data summary rather than hard coding the results. For example, the pandas shape method might be useful for calculating some of the summary results.
Provide a Basic Summary of the Data Set Using Python, Numpy and/or Pandas¶
### Replace each question mark with the appropriate value.
### Use python, pandas or numpy methods rather than hard coding the results
import numpy as np
# TODO: Number of training examples
n_train = X_train.shape[0]
# TODO: Number of testing examples.
n_test = X_test.shape[0]
# TODO: What's the shape of an traffic sign image?
image_shape = X_train.shape[1:]
# TODO: How many unique classes/labels there are in the dataset.
n_classes = np.unique(y_train).shape[0]
print("Number of training examples =", n_train)
print("Number of testing examples =", n_test)
print("Image data shape =", image_shape)
print("Number of classes =", n_classes)
Include an exploratory visualization of the dataset¶
Visualize the German Traffic Signs Dataset using the pickled file(s). This is open ended, suggestions include: plotting traffic sign images, plotting the count of each sign, etc.
The Matplotlib examples and gallery pages are a great resource for doing visualizations in Python.
NOTE: It's recommended you start with something simple first. If you wish to do more, come back to it after you've completed the rest of the sections.
### Data exploration visualization code goes here.
### Feel free to use as many code cells as needed.
import matplotlib.pyplot as plt
import random
from collections import Counter
import numpy as np
# Visualizations will be shown in the notebook.
%matplotlib inline
def visualize(X, y):
'''Represent an image per each class of sign and its label'''
fig = plt.figure(1)
#fig.suptitle(title, y=1.08, fontsize=14, fontweight='bold')
fig.set_size_inches(18.5, 30)
classes, indices = np.unique(y_train, return_index=True)
num_cols = 5
k = len(classes)
num_rows = k//num_cols
if (k%num_cols != 0):
num_rows += 1
count = 0
for i in indices:
count += 1
plt.subplot(num_rows, num_cols, count)
if (X.shape[3] == 1):
plt.imshow(X[i,:,:,0],cmap = 'gray')
else:
plt.imshow(X[i])
plt.title(y[i],fontsize=20)
plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=2.0)
plt.rc('font', size=10)
def plot_number_of_occurrencies(data):
'''Represent a bar chart in which each bar stands for a class
and its height show how many images of this class are in the dataset'''
c = Counter(data)
plt.bar(c.keys(), c.values())
plt.show()
#Visualize an image per each sign
visualize(X_train, y_train)
#Represent number of occurrencies
plt.figure(1)
plt.subplot(311)
plt.title('Training set')
plot_number_of_occurrencies(y_train)
plt.subplot(312)
plt.title('Validadion set')
plot_number_of_occurrencies(y_valid)
plt.subplot(313)
plt.title('Test set')
plot_number_of_occurrencies(y_test)
Step 2: Design and Test a Model Architecture¶
Design and implement a deep learning model that learns to recognize traffic signs. Train and test your model on the German Traffic Sign Dataset.
There are various aspects to consider when thinking about this problem:
- Neural network architecture
- Play around preprocessing techniques (normalization, rgb to grayscale, etc)
- Number of examples per label (some have more than others).
- Generate fake data.
Here is an example of a published baseline model on this problem. It's not required to be familiar with the approach used in the paper but, it's good practice to try to read papers like these.
NOTE: The LeNet-5 implementation shown in the classroom at the end of the CNN lesson is a solid starting point. You'll have to change the number of classes and possibly the preprocessing, but aside from that it's plug and play!
Pre-process the Data Set (normalization, grayscale, etc.)¶
Use the code cell (or multiple code cells, if necessary) to implement the first step of your project.
### Preprocess the data here. Preprocessing steps could include normalization, converting to grayscale, etc.
### Feel free to use as many code cells as needed.
#Define preprocessing functions
import cv2
import numpy as np
from numpy import newaxis
import scipy.ndimage
import random
def randrange_float(start, stop, step):
'''Returns a random float number between start and stop'''
return random.randint(0, int((stop - start) / step)) * step + start
def augment_data(X,y):
'''Taking into account the maximum number of occurrencies in the data set, this
function augments the number of occurrencies of each image up to the maximum rotating the
original images randomly'''
#Calculate number of different classes and number of occurrencies of each class
unique, counts = np.unique(y, return_counts=True)
#Maximum number of occurrencies
max_counts = max(counts)
for i in range(len(unique)):
#How many images we have to create
z = max_counts - counts[i]
new_features = []
new_labels = []
#Create a mask with the indices where the data set have images of the class unique[i](=i)
mask = np.where(y == i)
if z != 0:
for j in range(z):
#Choose the angle of rotation randomly
angle = randrange_float(-20, 20, 0.5)
#Choose one image of those which belong to this class (mask)
image = random.choice (X[mask])
#rotate the original image
new_features.append(scipy.ndimage.rotate(image, angle, reshape=False))
#create the new label for the new image
new_labels.append(i)
#Append new labels and new images to the original dataset
X = np.append(X, np.array(new_features), axis=0)
y = np.append(y, np.array(new_labels), axis=0)
return X, y
def normalize(image_data):
'''Returns the dataset in which the images have been normalized'''
a = 0.1
b = 0.9
current_min = 0
current_max = 255
return a + ( ( (image_data - current_min)*(b - a) )/( current_max - current_min ) )
#Augment and shuffle training data
from sklearn.utils import shuffle
#Normalize images from all the datasets: train, validation and test
X_train = normalize(X_train)
X_valid = normalize(X_valid)
X_test = normalize(X_test)
#Augment the data of the training set in order to have uniform distribution of occurrencies
X_train, y_train = augment_data(X_train, y_train)
#Shuffle the data
X_train, y_train = shuffle(X_train, y_train)
#Visualize the normalized images
visualize(X_train,y_train)
#Check number of occurrencies of the training set after augmenting data--Uniform distribution
#plt.title('Training set')
#plot_number_of_occurrencies(y_train)
plt.title('Training set')
plot_number_of_occurrencies(y_train)
Model Architecture¶
### Define your architecture here.
### Feel free to use as many code cells as needed.
import tensorflow as tf
#Define epochs and batch size
EPOCHS = 100
BATCH_SIZE = 128
from tensorflow.contrib.layers import flatten
def LeNet(x):
# Arguments used for tf.truncated_normal, randomly defines variables for the weights and biases for each layer
mu = 0
sigma = 0.1
#Layer 1: Convolutional. Input = 32x32x3. Output = 28x28x32.
conv1_W = tf.Variable(tf.truncated_normal((5, 5, 3, 32), mean = mu, stddev = sigma)) #(height, width, input_depth, output_depth)
conv1_b = tf.Variable(tf.zeros(32))
conv1 = tf.nn.conv2d(x, conv1_W, strides=[1, 1, 1, 1], padding='VALID')
conv1 = tf.nn.bias_add(conv1, conv1_b)
# RELU Activation.
conv1 = tf.nn.relu(conv1)
# Pooling. Input = 28x28x32. Output = 14x14x32.
conv1 = tf.nn.max_pool(conv1, ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1] , padding ='VALID')
# Layer 2: Convolutional. Output = 10x10x64.
conv2_W = tf.Variable(tf.truncated_normal((5, 5, 32, 64), mean = mu, stddev = sigma))
conv2_b = tf.Variable(tf.zeros(64))
conv2 = tf.nn.conv2d(conv1, conv2_W, strides=[1, 1, 1, 1], padding='VALID')
conv2 = tf.nn.bias_add(conv2, conv2_b)
# RELU Activation.
conv2 = tf.nn.relu(conv2)
# Pooling. Input = 10x10x64. Output = 5x5x64.
conv2 = tf.nn.max_pool(conv2, ksize = [1, 2, 2, 1] , strides = [1, 2, 2, 1], padding ='VALID')
# Flatten. Input = 5x5x64. Output = 1600.
fc0 = flatten(conv2)
# Layer 3: Fully Connected. Input = 1600. Output = 120.
fc1_W = tf.Variable(tf.truncated_normal((1600, 120), mean = mu, stddev = sigma))
fc1_b = tf.Variable(tf.zeros(120))
fc1 = tf.add(tf.matmul(fc0, fc1_W), fc1_b)
# RELU Activation.
fc1 = tf.nn.relu(fc1)
# Layer 4: Fully Connected. Input = 120. Output = 84.
fc2_W = tf.Variable(tf.truncated_normal((120, 84), mean = mu, stddev = sigma))
fc2_b = tf.Variable(tf.zeros(84))
fc2 = tf.add(tf.matmul(fc1, fc2_W), fc2_b)
# RELU Activation.
fc2 = tf.nn.relu(fc2)
#Add dropout to prevent overfitting
fc2 = tf.nn.dropout(fc2, keep_prob)
# Layer 5: Fully Connected. Input = 84. Output = 10.
fc3_W = tf.Variable(tf.truncated_normal((84, 43), mean = mu, stddev = sigma))
fc3_b = tf.Variable(tf.zeros(43))
logits = tf.add(tf.matmul(fc2, fc3_W), fc3_b)
return logits
Train, Validate and Test the Model¶
A validation set can be used to assess how well the model is performing. A low accuracy on the training and validation sets imply underfitting. A high accuracy on the test set but low accuracy on the validation set implies overfitting.
### Train your model here.
### Calculate and report the accuracy on the training and validation set.
### Once a final model architecture is selected,
### the accuracy on the test set should be calculated and reported as well.
### Feel free to use as many code cells as needed.
#Define placeholders
x = tf.placeholder(tf.float32, (None, 32, 32, 3))
y = tf.placeholder(tf.int32, (None))
keep_prob = tf.placeholder(tf.float32)
with tf.device('/cpu:0'):
one_hot_y = tf.one_hot(y, 43)
Training pipeline
#define learning rate
rate = 0.001
with tf.device('/gpu:0'):
logits = LeNet(x)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits, one_hot_y)
loss_operation = tf.reduce_mean(cross_entropy)
optimizer = tf.train.AdamOptimizer(learning_rate = rate)
training_operation = optimizer.minimize(loss_operation)
Evaluation
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_y, 1))
accuracy_operation = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
saver = tf.train.Saver()
def evaluate(X_data, y_data):
num_examples = len(X_data)
total_accuracy = 0
sess = tf.get_default_session()
for offset in range(0, num_examples, BATCH_SIZE):
batch_x, batch_y = X_data[offset:offset+BATCH_SIZE], y_data[offset:offset+BATCH_SIZE]
accuracy = sess.run(accuracy_operation, feed_dict={x: batch_x, y: batch_y, keep_prob: 1.0})
total_accuracy += (accuracy * len(batch_x))
return total_accuracy / num_examples
Train the model
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
sess.run(tf.global_variables_initializer())
num_examples = len(X_train)
#saver.restore(sess, './lenet2')
print("Training...")
print()
for i in range(EPOCHS):
X_train, y_train = shuffle(X_train, y_train)
for offset in range(0, num_examples, BATCH_SIZE):
end = offset + BATCH_SIZE
batch_x, batch_y = X_train[offset:end], y_train[offset:end]
sess.run(training_operation, feed_dict={x: batch_x, y: batch_y, keep_prob: 0.5})
validation_accuracy = evaluate(X_valid, y_valid)
print("EPOCH {} ...".format(i+1))
print("Validation Accuracy = {:.3f}".format(validation_accuracy))
print()
saver.save(sess, './lenet3')
print("Model saved")
Evaluate the model with the test set
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
saver.restore(sess,'./lenet3')
test_accuracy = evaluate(X_test, y_test)
print("Test Accuracy = {:.3f}".format(test_accuracy))
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
saver.restore(sess,'./lenet3')
test_accuracy = evaluate(X_train, y_train)
print("Training Accuracy = {:.3f}".format(test_accuracy))
#Calculate the predictions for the test set to represent the confusion matrix
test_pred = tf.nn.softmax(logits)
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
saver.restore(sess,'./lenet3')
num_examples = len(X_test)
preds = []
for offset in range(0, num_examples, BATCH_SIZE):
batch_x = X_test[offset:offset+BATCH_SIZE]
test_preds = sess.run(test_pred, feed_dict={x: batch_x, keep_prob: 1.0})
test_preds = [np.argmax(row) for row in test_preds]
preds.append(test_preds)
#flatten
preds = sum(preds, [])
from pylab import rcParams
from sklearn.metrics import confusion_matrix
#Represent the confusion matrix
cm = confusion_matrix(y_test, preds)
n_classes=43
plt.figure(figsize=(40,40))
rcParams['figure.figsize'] = 13, 13
plt.matshow(cm)
plt.colorbar()
tick_marks = np.arange(n_classes)
plt.xticks(tick_marks, range(n_classes))
plt.yticks(tick_marks, range(n_classes))
plt.xlabel('Predicted')
plt.ylabel('True')
plt.show()
Step 3: Test a Model on New Images¶
To give yourself more insight into how your model is working, download at least five pictures of German traffic signs from the web and use your model to predict the traffic sign type.
You may find signnames.csv useful as it contains mappings from the class id (integer) to the actual sign name.
Load and Output the Images¶
### Load the images and plot them here.
### Feel free to use as many code cells as needed.
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import cv2
import numpy as np
%matplotlib inline
def display_images(images):
'''function to plot images'''
fig = plt.figure(1)
#fig.suptitle(title, y=1.08, fontsize=14, fontweight='bold')
fig.set_size_inches(18.5, 30)
num_cols = 5
k = len(images)
num_rows = k//num_cols
if (k%num_cols != 0):
num_rows += 1
count = 0
for i in images:
count += 1
plt.subplot(num_rows, num_cols, count)
plt.imshow(i)
plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=2.0)
plt.rc('font', size=10)
def read_images(paths):
'''Read images from paths. Return an array with images with shape 32x32'''
images = []
new_size=32
for imgname in paths:
#read images
img=mpimg.imread('images/'+ imgname)
#resize images to 32x32
img= cv2.resize(img,(new_size,new_size))
images.append(img)
images = np.array(images)
return images
#Create a list with the names of the images
paths = ['20.jpg', '80.jpg', '30.jpg', 'exclamation.jpg', 'sign5.jpg']
#Load images from file
images = read_images(paths)
#Dislay images
display_images(images)
#Preprocess the images normalizing them
images = normalize(images)
#Display normalized images
display_images(images)
#Define X dataset
x_collected = images
#Define y dataset (labels for images in X dataset)
y_collected_true = np.array([0, 5, 1, 18, 17])
Predict the Sign Type for Each Image¶
### Run the predictions here and use the model to output the prediction for each image.
### Make sure to pre-process the images with the same pre-processing pipeline used earlier.
### Feel free to use as many code cells as needed.
import tensorflow as tf
with tf.device('/gpu:0'):
new_pred = tf.nn.softmax(logits)
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
saver.restore(sess,'./lenet3')
test_preds = sess.run(new_pred, feed_dict={x: x_collected, keep_prob: 1.0})
top_k_preds = sess.run(tf.nn.top_k(test_preds , k=5))
print([np.argmax(row) for row in test_preds])
Analyze Performance¶
### Calculate the accuracy for these 5 new images.
### For example, if the model predicted 1 out of 5 signs correctly, it's 20% accurate on these new images.
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
saver.restore(sess,'./lenet3')
test_accuracy = evaluate(x_collected, y_collected_true)
print("Accuracy = {:.3f}".format(test_accuracy))
Output Top 5 Softmax Probabilities For Each Image Found on the Web¶
For each of the new images, print out the model's softmax probabilities to show the certainty of the model's predictions (limit the output to the top 5 probabilities for each image). tf.nn.top_k could prove helpful here.
The example below demonstrates how tf.nn.top_k can be used to find the top k predictions for each image.
tf.nn.top_k will return the values and indices (class ids) of the top k predictions. So if k=3, for each sign, it'll return the 3 largest probabilities (out of a possible 43) and the correspoding class ids.
Take this numpy array as an example. The values in the array represent predictions. The array contains softmax probabilities for five candidate images with six possible classes. tk.nn.top_k is used to choose the three classes with the highest probability:
# (5, 6) array
a = np.array([[ 0.24879643, 0.07032244, 0.12641572, 0.34763842, 0.07893497,
0.12789202],
[ 0.28086119, 0.27569815, 0.08594638, 0.0178669 , 0.18063401,
0.15899337],
[ 0.26076848, 0.23664738, 0.08020603, 0.07001922, 0.1134371 ,
0.23892179],
[ 0.11943333, 0.29198961, 0.02605103, 0.26234032, 0.1351348 ,
0.16505091],
[ 0.09561176, 0.34396535, 0.0643941 , 0.16240774, 0.24206137,
0.09155967]])Running it through sess.run(tf.nn.top_k(tf.constant(a), k=3)) produces:
TopKV2(values=array([[ 0.34763842, 0.24879643, 0.12789202],
[ 0.28086119, 0.27569815, 0.18063401],
[ 0.26076848, 0.23892179, 0.23664738],
[ 0.29198961, 0.26234032, 0.16505091],
[ 0.34396535, 0.24206137, 0.16240774]]), indices=array([[3, 0, 5],
[0, 1, 4],
[0, 5, 1],
[1, 3, 5],
[1, 4, 3]], dtype=int32))Looking just at the first row we get [ 0.34763842, 0.24879643, 0.12789202], you can confirm these are the 3 largest probabilities in a. You'll also notice [3, 0, 5] are the corresponding indices.
print (top_k_preds)
### Print out the top five softmax probabilities for the predictions on the German traffic sign images found on the web.
### Feel free to use as many code cells as needed.
import pandas as pd
import pandas as pd
sign_names = pd.read_csv('signnames.csv')
def get_sign_name(class_index):
row = sign_names[sign_names.ClassId == int(class_index)]
return row.SignName.values[0]
def get_sign_names(indices):
return [get_sign_name(index) for index in indices]
#Plot each image with a bar chart showing the probabilites assigned to the top 5 predictions
for i, image in enumerate(images):
# Plot the original image
plt.subplot(121);
plt.imshow(image);
plt.title('Image');
# Get the top k probabilities and the classes to which they correspond
indices = top_k_preds.indices[i]
values = top_k_preds.values[i]
# Sort in ascending order
idx = values.argsort()
values = values[idx]
indices = indices[idx]
# Show a bar chart of the probabilities
y_pos = range(0, 200, 40)
plt.subplot(122)
plt.bar(y_pos, values, align='center', alpha=0.5, color='red', width=20)
plt.xticks(y_pos, get_sign_names(indices))
locs, labels = plt.xticks()
plt.setp(labels, rotation=90)
plt.title('Probability of top 5 classes')
plt.show()
Note: Once you have completed all of the code implementations, you need to finalize your work by exporting the IPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run. You can then export the notebook by using the menu above and navigating to \n", "File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.